This following case report describes the open reduction, internal fixation and the reconstruction of an extensive comminuted mandibular fracture with bilateral condylar fractures in a 19-year-old male patient with an intellectual disability and autistic disorder. He suffered fall trauma, resulting in shattered bony fragments of the alveolus and mandibular body between both mandibular rami, the fracture of both condyles and the avulsion or dislocation of every posterior tooth of the mandible. The patient underwent open reduction and internal fixation between both mandibular rami using a reconstruction plate, open reduction and internal fixation of the shattered fragments using miniplates and screws, and the closed reduction of the bilateral condylar fractures.

Clinical features of masticator-space abscess (MSA) are very similar to those of parotitis or temporomandibular disorder (TMD), making early differential diagnosis difficult. Local causes of MSA include nerve block anesthesia, infection after tooth extraction, and trauma to the temporomandibular joint (TMJ); the systemic cause is immunodeficiency. Odontogenic causes account for most etiologies, but there are also unusual causes of MSA. A 66-year-old male patient visited the emergency room (ER) presenting with left-side TMJ pain three days after receiving an acupressure massage. He was tentatively diagnosed with conventional post-trauma TMD and discharged with medication. However, the patient returned to the ER with increased pain. At this time, his TMD diagnosis was confirmed. He made a third visit to the ER during which facial computed tomographic (CT) images were taken. CT readings identified an abscess or hematoma in the left masticator space. After hospitalizing the patient, needle aspiration confirmed pus in the infratemporal and temporal fossa. Antibiotics were administered, and the abscess was drained through an incision made by the attending physician. The patient's symptoms decreased, and he was discharged.

In recent years, healthcare has gained unprecedented attention from researchers in the field of Human health science and technology. Oral health, a subdomain of healthcare described as being very complex, is threatened by diseases like dental caries, gum disease, oral cancer, etc. The critical point is to propose an identification mechanism to prevent the population from being affected by these diseases. The large amount of online data allows scholars to perform tremendous research on health conditions, specifically oral health. Regardless of the high-performing dental consultation tools available in current healthcare, computer-based technology has shown the ability to complete some tasks in less time and cost less than when using similar healthcare tools to perform the same type of work. Machine learning has displayed a wide variety of advantages in oral healthcare, such as predicting dental caries in the population. Compared to the standard dental caries prediction previously proposed, this work emphasizes the importance of using multiple data sources, referred to as multi-modality, to extract more features and obtain accurate performances. The proposed prediction model constructed using multi-modal data demonstrated promising performances with an accuracy of 90%, F1-score of 89%, a recall of 90%, and a precision of 89%.

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind’s daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site’s energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings.

| Abstract |1 )PURPOSE: The purpose of this study was to investigate the effect of a physical therapy-based tailored exercise program on pain, accident incidence rates, the number of work days lost, and economical loss cost for workers in an automobile parts manufacturing company. METHODS: RESULTS:After applying the exercise program, pain decreased and the number of workers participating in the program increased. Accident incidence rates, number of work days lost, and economical loss cost decreased. There was a significant correlation between the number of workers who received exercise therapy by year and accident incidence rates, lost days of work, and economical loss cost (p<.05). CONCLUSION:It is necessary to expand the physical therapy-based tailored exercise program to prevent musculoskeletal disorders because it has a positive effect on both workers and employers.